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129
Concept Decompositions for Large Sparse Text Data using Clustering
 Machine Learning
, 2000
"... . Unlabeled document collections are becoming increasingly common and available; mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as highdimensional and sparse vectorsa few thousand dimensions and a sparsity of 95 to 99 ..."
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Cited by 401 (27 self)
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. Unlabeled document collections are becoming increasingly common and available; mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as highdimensional and sparse vectorsa few thousand dimensions and a sparsity of 95 to 99% is typical. In this paper, we study a certain spherical kmeans algorithm for clustering such document vectors. The algorithm outputs k disjoint clusters each with a concept vector that is the centroid of the cluster normalized to have unit Euclidean norm. As our first contribution, we empirically demonstrate that, owing to the highdimensionality and sparsity of the text data, the clusters produced by the algorithm have a certain "fractallike" and "selfsimilar" behavior. As our second contribution, we introduce concept decompositions to approximate the matrix of document vectors; these decompositions are obtained by taking the leastsquares approximation onto the linear subspace spanned...
Survey of clustering data mining techniques
, 2002
"... Accrue Software, Inc. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in math ..."
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Cited by 400 (0 self)
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Accrue Software, Inc. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Clustering is the subject of active research in several fields such as statistics, pattern recognition, and machine learning. This survey focuses on clustering in data mining. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique
Data Clustering: 50 Years Beyond KMeans
, 2008
"... Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and m ..."
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Cited by 274 (6 self)
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Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is exploratory in nature to find structure in data. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, Kmeans, was first published in 1955. In spite of the fact that Kmeans was proposed over 50 years ago and thousands of clustering algorithms have been published since then, Kmeans is still widely used. This speaks to the difficulty of designing a general purpose clustering algorithm and the illposed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semisupervised clustering, ensemble clustering, simultaneous feature selection, and data clustering and large scale data clustering.
PrivacyPreserving KMeans Clustering over Vertically Partitioned Data
 IN SIGKDD
, 2003
"... Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for kmeans cl ..."
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Cited by 159 (9 self)
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Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for kmeans clustering when different sites contain different attributes for a common set of entities. Each site learns the cluster of each entity, but learns nothing about the attributes at other sites.
Collective Data Mining: A New Perspective Toward Distributed Data Analysis
 Advances in Distributed and Parallel Knowledge Discovery
, 1999
"... This paper introduces the collective data mining (CDM) framework, a new approach toward distributed data mining (DDM) from heterogeneous sites. It points out that naive approaches to distributed data analysis in a heterogeneous environment may result in ambiguous or incorrect global data models. It ..."
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Cited by 104 (15 self)
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This paper introduces the collective data mining (CDM) framework, a new approach toward distributed data mining (DDM) from heterogeneous sites. It points out that naive approaches to distributed data analysis in a heterogeneous environment may result in ambiguous or incorrect global data models. It also notes that any function can be expressed in a distributed fashion using a set of appropriate basis functions and orthogonal basis functions can be eectively used for developing a general DDM framework that guarantees correct local analysis and correct aggregation of local data models with minimal data communication. This paper develops the foundation of CDM, discusses decision tree learning and polynomial regression in CDM for discrete and continuous variables, and describes the BODHI, a CDMbased experimental system for distributed knowledge discovery. 1 Introduction Distributed data mining (DDM) is a fast growing area that deals with the problem of nding data patterns in a...
Information retrieval on the Web
 ACM Computing Surveys
, 2000
"... In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. We present data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. Although numerical ..."
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Cited by 91 (0 self)
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In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. We present data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. Although numerical figures vary, overall trends cited
Distributed Data Mining: Algorithms, Systems, and Applications
, 2002
"... This paper presents a brief overview of the DDM algorithms, systems, applications, and the emerging research directions. The structure of the paper is organized as follows. We first present the related research of DDM and illustrate data distribution scenarios. Then DDM algorithms are reviewed. Subs ..."
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Cited by 66 (5 self)
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This paper presents a brief overview of the DDM algorithms, systems, applications, and the emerging research directions. The structure of the paper is organized as follows. We first present the related research of DDM and illustrate data distribution scenarios. Then DDM algorithms are reviewed. Subsequently, the architectural issues in DDM systems and future directions are discussed
Privacypreserving Distributed Clustering using Generative Models
, 2003
"... We present a framework for clustering distributed data in unsupervised and semisupervised scenarios, taking into account privacy requirements and communication costs. Rather than sharing parts of the original or perturbed data, we instead transmit the parameters of suitable generative models built ..."
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Cited by 64 (1 self)
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We present a framework for clustering distributed data in unsupervised and semisupervised scenarios, taking into account privacy requirements and communication costs. Rather than sharing parts of the original or perturbed data, we instead transmit the parameters of suitable generative models built at each local data site to a central location. We mathematically show that the best representative of all the data is a certain " mean" model, and empirically show that this model can be approximated quite well by generating artificial samples from the underlying distributions using Markov Chain Monte Carlo techniques, and then fitting a combined global model with a chosen parametric form to these samples. We also propose a new measure that quantifies privacy based on information theoretic concepts, and show that decreasing privacy leads to a higher quality of the combined model and vice versa. We provide empirical results on different data types to highlight the generality of our framework. The results show that high quality distributed clustering can be achieved with little privacy loss and low communication cost.
1 Parallel Spectral Clustering in Distributed Systems
"... Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms such as kmeans. However, spectral clustering suffers from a scalability problem in both memory use and computational time when the size of a data set is large. To perform cluster ..."
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Cited by 61 (1 self)
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Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms such as kmeans. However, spectral clustering suffers from a scalability problem in both memory use and computational time when the size of a data set is large. To perform clustering on large data sets, we investigate two representative ways of approximating the dense similarity matrix. We compare one approach by sparsifying the matrix with another by the Nyström method. We then pick the strategy of sparsifying the matrix via retaining nearest neighbors and investigate its parallelization. We parallelize both memory use and computation on distributed computers. Through
Shared Memory Parallelization of Data Mining Algorithms: Techniques, Programming Interface, and Performance
 In Proceedings of the second SIAM conference on Data Mining
, 2002
"... With recent technological advances, shared memory parallel machines have become more scalable, and oer large main memories and high bus bandwidths. They are emerging as good platforms for data warehousing and data mining. In this paper, we focus on shared memory parallelization of data mining alg ..."
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Cited by 41 (15 self)
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With recent technological advances, shared memory parallel machines have become more scalable, and oer large main memories and high bus bandwidths. They are emerging as good platforms for data warehousing and data mining. In this paper, we focus on shared memory parallelization of data mining algorithms.